Pandawa, Nur Sukma (2023) Adaptive Weighting of Oil Quality Index on Power Transformers Using Particle Swarm Optimization. Diploma thesis, Politeknik Negeri Malang.
![[img]](http://repota.jti.polinema.ac.id/style/images/fileicons/text.png) |
Text
COVER.pdf Restricted to Registered users only Download (1MB) |
|
Text
BAB I.pdf Restricted to Registered users only Download (142kB) |
|
Text
BAB II.pdf Restricted to Registered users only Download (385kB) |
|
Text
BAB II.pdf Restricted to Registered users only Download (385kB) |
|
Text
BAB III.pdf Restricted to Registered users only Download (645kB) |
|
Text
BAB IV.pdf Restricted to Registered users only Download (776kB) |
|
Text
BAB V.pdf Restricted to Registered users only Download (1MB) |
|
Text
BAB VI.pdf Restricted to Registered users only Download (159kB) |
|
Text
BAB VII.pdf Restricted to Registered users only Download (78kB) |
|
Text
DAFTAR PUSTAKA.pdf Restricted to Registered users only Download (95kB) |
|
Text
LAMPIRAN.pdf Restricted to Registered users only Download (501kB) |
Abstract
In the electrical substation system circuit, there is one equipment that plays an important role in the electricity generation and distribution system, namely the power transformer. Therefore, proper maintenance is an important factor in maintaining the function and reliability of power transformers. To keep the function of the power transformer optimal, the quality of the oil in the power transformer as a transformer cooling factor needs to be considered. In determining the oil quality index in the transformer, measurements are needed on 5 variables, namely, dielectric strength (BDV), water (ppm), acidity (ACID), Index Quality Factor (IFT), and color (cl). In the process of calculating the oil quality index, the weight of the index value is required. Unfortunately, there is no exact standard weight value used for these 5 variables and only depends on expert researchers. This problem can be solved by optimization methods such as the Particle Swarm Optimization (PSO) algorithm. The PSO algorithm will search and determine weights adaptively depending on the situation and state of the data used as input. So it will save time to determine the oil quality index and take maintenance steps. The optimal parameters of the research results are the number of iterations 186 and population size 65. The optimal parameter results will be applied to the data to determine accuracy using the Mean Absolute Percentage Error (MAPE) method. Based on the MAPE calculation, the average result is 6.44% from 10 tests. Keywords : Particle Swarm Optimization (PSO) algorithm, Oil Quality Index, Power Transformer, Adaptive Weighting
| Item Type: | Thesis (Diploma) |
|---|---|
| Subjects: | A Computer Science > Computer Programming A Computer Science > Information Science A Computer Science > Networking A Computer Science > Operating Systems A Computer Science > Theory, Logic and Design A Computer Science > Applied Computer Science A Computer Science > Internet Of Things |
| Divisions: | Jurusan Teknologi Informasi > Teknik Informatika |
| Depositing User: | nur sukma pandawa |
| Date Deposited: | 09 May 2024 11:34 |
| Last Modified: | 09 May 2024 11:34 |
| URI: | http://repota.jti.polinema.ac.id/id/eprint/940 |
Actions (login required)
 |
View Item |